Evaluation of Document-Level Text Simplification in Japanese

Abstract

This study establishes an evaluation framework for document-level text simplification in Japanese by constructing a human-annotated dataset and examining the reliability of LLM-based automatic evaluation. We first developed detailed annotation guidelines covering four criteria—necessity, sufficiency, sentence-level simplicity, and document-level simplicity—and collected human ratings for 1,128 source–target document pairs derived from the Wikipedia part of the Japanese simplification corpus JADOS. Using this dataset, we conducted extensive experiments comparing human judgments with evaluations from large language models, including GPT, Claude, and Gemini. The results show that GPT-4o and Gemini 2.5 Pro achieve high agreement with human annotators even in the 0-shot setting, demonstrating their potential as reliable automatic evaluators for Japanese simplification. However, LLMs exhibited a consistent tendency to underestimate document-level simplicity, particularly for kanji-dense texts or texts with relatively long sentences and a small number of sentences. This work provides the first benchmark for evaluating document-level text simplification in Japanese and offers practical evidence that LLM-based evaluation can support scalable assessment for Japanese document-level simplification.

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